The DREAM BIG project as a model for harmonizing early measures of parental care and parent-child interactions across epidemiological cohorts
Bibliographic record
Abstract
Parenting is a key contributor to child development. The effects of parenting, however, also depend on child characteristics, including genetic factors. A more complete appraisal of the role of parenting thus requires a comprehensive developmental model which explores questions about parenting behavior, child susceptibility to parenting, and child psychopathology. Moving forward, we need to not only be concerned about sample sizes that limit testing of comprehensive models but also the need to replicate findings across multiple settings and samples. A consortium which harmonises key measures offers the opportunity to examine these questions. The Developmental Research in Environmental Adversity, Mental health, BIological susceptibility and Gender (DREAM BIG) consortium includes six international longitudinal prospective birth cohorts to explore the early life origins of major psychiatric disorders in childhood. Here, we will provide a brief overview of parental care research, methodological limitations, and two exciting recent attempts (i.e., the DREAM BIG consortium and the CATS-project), that address key methodological challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.121 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".